Data analytics system to automatically recommend risk mitigation strategies for an enterprise

Abstract

A data analytics system may include a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider. Similarly, a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider. A back-end application computer server may include a data mining engine that analyzes a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers. A predictive analytics engine may then calculate a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers. An insight platform may automatically generate a recommended action for the enterprise to lower the calculated risk score.

What is claimed: 
     
       1. A data analytics system implemented via a back-end application computer server, comprising:
 (a) a first risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and, for each risk relationship, an electronic record identifier and a set of entity attribute values including an entity identifier; 
 (b) a second risk relationship data store containing electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider, and, for each risk relationship, an electronic record identifier and a set of entity attribute values including an entity identifier; 
 (c) the back-end application computer server, coupled to the risk relationship data store, including: 
 a computer processor, and 
 a computer memory, coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to:
 analyze, by a data mining engine, a set of electronic records in the first and second risk relationship data stores to identify flags corresponding to risk drivers, wherein said analyzing is performed without retrieving other electronic records, and combining the retrieved information with third-party data received from a third-party data source, to reduce a number of electronic records transmitted via a distributed communication network, 
 calculate, by a predictive analytics engine, a risk score associated with the set of electronic records based on the associated entity attribute values and the identified flags corresponding to risk drivers, and 
 automatically generate, by an insight platform, a recommended action for the enterprise to lower the calculated risk score; 
 
 (d) a communication port coupled to the back-end application computer server to facilitate a transmission of data with remote user devices to support interactive user interface displays via the distributed communication network; 
 (e) a calendar function, coupled to the back-end application computer server, to automatically generate a reminder that the recommended action generated by the insight platform should be implemented; and 
 (f) an email server, coupled to the calendar function, to automatically establish a channel of communication with an entity linked with the entity identifier and transmit a message, including the reminder generated by the calendar function, via the established channel of communication. 
 
     
     
       2. The system of  claim 1 , wherein at least one of the first and second risk relationship data stores is associated with one of: (i) health insurance, (ii) prescription pharmacy insurance, (iii) workers' compensation insurance, (iv) paid family leave insurance, (v) disability insurance, (vi) short term disability insurance, (vii) long term disability insurance, (viii) paid time off, (ix) sick leave, (x) employee sentiment, (xi) human resources data, and (xii) wearable Internet of Things (“IoT”) sensors. 
     
     
       3. The system of  claim 1 , wherein the automatically generated recommendation is associated with at least one of: (i) insurance pricing, (ii) a deductible, (iii) a limit, (iv) an insurance plan design, (v) insurance claim management, (vi) a service, (vii) a prevention strategy, and (viii) a recovery strategy. 
     
     
       4. The system of  claim 1 , wherein the predictive analytics engine implements a predictive model to calculate the likelihood of certain events occurring on the basis of risk drivers identified for each of the plurality of electronic records; and
 wherein the risk score is based on the calculated likelihood of certain events occurring. 
 
     
     
       5. The system of  claim 1 , wherein the insight platform accesses a database of insurance claim records, each insurance claim record including associated risk score and claim outcome; and
 wherein the insight platform determines an expected claim outcome for the calculated risk score by analyzing the claim outcomes of insurance claim records having risk scores that are substantially the same as the calculated risk score. 
 
     
     
       6. The system of  claim 1 , wherein the insight platform automatically generates an electronic message requesting confirmation that the recommended action has been implemented. 
     
     
       7. The system of  claim 1 , wherein the insight platform generates an insurance claim record corresponding to each of the plurality of electronic records, each insurance claim record including an associated risk score and an expected claim outcome. 
     
     
       8. A computerized data analytics method implemented via a back-end application computer server, comprising:
 analyzing, by a computer processor executing a data mining engine of the back-end application computer server, a set of electronic records in a first and a second risk relationship data store to identify flags corresponding to risk drivers, wherein the first risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and the second risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider, wherein said analyzing is performed without retrieving other electronic records, and combining the set of electronic records with third-party data received from a third-party data source, to reduce a number of electronic records transmitted via a distributed communication network; 
 calculating, by a predictive analytics engine of the back-end application computer server, a risk score associated with the set of electronic records based on associated entity attribute values and the identified flags corresponding to risk drivers; 
 automatically generating, by an insight platform of the back-end application computer server, a recommended action for the enterprise to lower the calculated risk score; 
 automatically generating, by a calendar function coupled to the back-end application computer server, a reminder that the recommended action generated by the insight platform should be implemented; and 
 automatically establishing, by an email server coupled to the calendar function, a channel of communication with an entity linked with the entity identifier and transmit a message, including the reminder generated by the calendar function, via the established channel of communication. 
 
     
     
       9. The method of  claim 8 , wherein at least one of the first and second risk relationship data stores is associated with one of: (i) health insurance, (ii) prescription pharmacy insurance, (iii) workers' compensation insurance, (iv) paid family leave insurance, (v) disability insurance, (vi) short term disability insurance, (vii) long term disability insurance, (viii) paid time off, (ix) sick leave, (x) employee sentiment, (xi) human resources data, and (xii) wearable Internet of Things (“IoT”) sensors. 
     
     
       10. The method of  claim 8 , wherein the automatically generated recommendation is associated with at least one of: (i) insurance pricing, (ii) a deductible, (iii) a limit, (iv) an insurance plan design, (v) insurance claim management, (vi) a service, (vii) a prevention strategy, and (viii) a recovery strategy. 
     
     
       11. The method of  claim 8 , wherein the predictive analytics engine implements a predictive model to calculate the likelihood of certain events occurring on the basis of risk drivers identified for each of the plurality of electronic records; and
 wherein the risk score is based on the calculated likelihood of certain events occurring. 
 
     
     
       12. The method of  claim 8 , wherein the insight platform accesses a database of insurance claim records, each insurance claim record including associated risk score and claim outcome; and
 wherein the insight platform determines an expected claim outcome for the calculated risk score by analyzing the claim outcomes of insurance claim records having risk scores that are substantially the same as the calculated risk score. 
 
     
     
       13. The method of  claim 8 , wherein the insight platform automatically generates an electronic message requesting confirmation that the recommended action has been implemented. 
     
     
       14. The method of  claim 8 , wherein the insight platform generates an insurance claim record corresponding to each of the plurality of electronic records, each insurance claim record including an associated risk score and an expected claim outcome. 
     
     
       15. A non-tangible, computer-readable medium storing instructions, that, when executed by a processor, cause the processor to perform a data analytics method implemented via a back-end application computer server, the method comprising:
 analyzing, by a computer processor executing a data mining engine of the back-end application computer server, a set of electronic records in a first and a second risk relationship data store to identify flags corresponding to risk drivers, wherein the first risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a first risk relationship provider, and the second risk relationship data store contains electronic records that represent a plurality of risk relationships between the enterprise and a second risk relationship provider, wherein said analyzing is performed without retrieving other electronic records, and combining the set of electronic records with third-party data received from a third-party data source, to reduce a number of electronic records transmitted via a distributed communication network; 
 calculating, by a predictive analytics engine of the back-end application computer server, a risk score associated with the set of electronic records based on associated entity attribute values and the identified flags corresponding to risk drivers; 
 automatically generating, by an insight platform of the back-end application computer server, a recommended action for the enterprise to lower the calculated risk score; 
 automatically generating, by a calendar function coupled to the back-end application computer server, a reminder that the recommended action generated by the insight platform should be implemented; and 
 automatically establishing, by an email server coupled to the calendar function, a channel of communication with an entity linked with the entity identifier and transmit a message, including the reminder generated by the calendar function, via the established channel of communication. 
 
     
     
       16. The method of  claim 15 , wherein at least one of the first and second risk relationship data stores is associated with one of: (i) health insurance, (ii) prescription pharmacy insurance, (iii) workers' compensation insurance, (iv) paid family leave insurance, (v) disability insurance, (vi) short term disability insurance, (vii) long term disability insurance, (viii) paid time off, (ix) sick leave, (x) employee sentiment, (xi) human resources data, and (xii) wearable Internet of Things (“IoT”) sensors. 
     
     
       17. The medium of  claim 15 , wherein the automatically generated recommendation is associated with at least one of: (i) insurance pricing, (ii) a deductible, (iii) a limit, (iv) an insurance plan design, (v) insurance claim management, (vi) a service, (vii) a prevention strategy, and (viii) a recovery strategy. 
     
     
       18. The medium of  claim 15 , wherein the predictive analytics engine implements a predictive model to calculate the likelihood of certain events occurring on the basis of risk drivers identified for each of the plurality of electronic records; and
 wherein the risk score is based on the calculated likelihood of certain events occurring. 
 
     
     
       19. The medium of  claim 15 , wherein the insight platform accesses a database of insurance claim records, each insurance claim record including associated risk score and claim outcome; and
 wherein the insight platform determines an expected claim outcome for the calculated risk score by analyzing the claim outcomes of insurance claim records having risk scores that are substantially the same as the calculated risk score. 
 
     
     
       20. The medium of  claim 15 , wherein the insight platform automatically generates an electronic message requesting confirmation that the recommended action has been implemented. 
     
     
       21. The medium of  claim 15 , wherein the insight platform generates an insurance claim record corresponding to each of the plurality of electronic records, each insurance claim record including an associated risk score and an expected claim outcome.